Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks

Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks

Abstract The belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of indu...

Full description

Bibliographic Details
Main Authors: Mohammad Siami, Tomasz Barszcz, Jacek Wodecki, Radoslaw Zimroz
Format: Article
Language:English
Published: Nature Portfolio 2024-03-01
Series:Scientific Reports
Subjects:
U-Net
Convolutional neural networks
Semantic segmentation
Thermal imaging
Conveyor systems
Idlers
Online Access:https://doi.org/10.1038/s41598-024-55864-2

Internet

https://doi.org/10.1038/s41598-024-55864-2

Similar Items